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Identifying incompleteness in privacy policy goals using semantic frames
Requirements Engineering ( IF 2.8 ) Pub Date : 2019-06-11 , DOI: 10.1007/s00766-019-00315-y
Jaspreet Bhatia , Morgan C. Evans , Travis D. Breaux

Companies that collect personal information online often maintain privacy policies that are required to accurately reflect their data practices and privacy goals. To be comprehensive and flexible for future practices, policies contain ambiguity that summarizes practices over multiple types of products and business contexts. Ambiguity in data practice descriptions undermines policies as an effective way to communicate system design choices to users and as a reliable regulatory mechanism. In this paper, we report an investigation to identify incompleteness by representing data practice descriptions as semantic frames. The approach is a grounded analysis to discover which semantic roles corresponding to a data action are needed to construct complete data practice descriptions. Our results include 698 data action instances obtained from 949 manually annotated statements across 15 privacy policies and three domains: health, news and shopping. Therein, we identified 2316 instances of 17 types of semantic roles and found that the distribution of semantic roles across the three domains was similar. Incomplete data practice descriptions undermine user comprehension and can affect the user’s perceived privacy risk, which we measure using factorial vignette surveys. We observed that user risk perception decreases when two roles are present in a statement: the condition under which a data action is performed, and the purpose for which the user’s information is used.

中文翻译:

使用语义框架识别隐私政策目标的不完整性

在线收集个人信息的公司通常会维护隐私政策,以准确反映其数据实践和隐私目标。为了对未来的实践更加全面和灵活,政策包含概括多种类型产品和业务环境的实践的歧义。数据实践描述中的歧义破坏了政策作为向用户传达系统设计选择的有效方式和可靠的监管机制。在本文中,我们报告了一项通过将数据实践描述表示为语义框架来识别不完整性的调查。该方法是一种扎根分析,以发现需要哪些与数据操作相对应的语义角色来构建完整的数据实践描述。我们的结果包括从 15 个隐私政策和三个领域(健康、新闻和购物)的 949 个手动注释语句中获得的 698 个数据操作实例。其中,我们识别了 17 种语义角色的 2316 个实例,发现三个领域的语义角色分布相似。不完整的数据实践描述会破坏用户的理解,并可能影响用户感知的隐私风险,我们使用因子小插图调查来衡量。我们观察到,当语句中存在两个角色时,用户风险感知会降低:执行数据操作的条件,以及使用用户信息的目的。我们确定了 17 种语义角色的 2316 个实例,发现三个领域的语义角色分布相似。不完整的数据实践描述会破坏用户的理解,并可能影响用户感知的隐私风险,我们使用因子小插图调查来衡量。我们观察到,当语句中存在两个角色时,用户风险感知会降低:执行数据操作的条件,以及使用用户信息的目的。我们确定了 17 种语义角色的 2316 个实例,发现三个领域的语义角色分布相似。不完整的数据实践描述会破坏用户的理解,并可能影响用户感知的隐私风险,我们使用因子小插图调查来衡量。我们观察到,当语句中存在两个角色时,用户风险感知会降低:执行数据操作的条件,以及使用用户信息的目的。
更新日期:2019-06-11
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